These circuits are multi-cell networks configured so as to imitate somewhat the behavior of biological neural networks. Biological neural networks comprise elementary neurons which receive and emit information, and synapses which connect these neurons to other neurons. By analogy, neuromorphic circuits generally comprise a matrix network of elementary processing cells that will be called neurons, each identified by a respective address in the memory, and a matrix memory of as many elementary memories as there are neurons; each elementary memory is associated with a neuron and can therefore be identified by the unique address of this neuron; it contains addresses of other neurons which must receive an item of information originating from the neuron corresponding to this elementary memory.
So-called “discharge” neurons are considered in what follows. These neurons receive input signals originating from other neurons; they process them in analog form generally and produce a result. The result can be the emission of an event signal, for example a pulse at a given moment. It is this so-called “neuron discharge” pulse which serves to fetch from the elementary memory associated with the neuron not only the addresses of other neurons (target neurons or destination neurons), but also weights associated with each of these addresses. The associated weights signify that one neuron will influence one or more other neurons in a weighted manner and not in an undifferentiated manner.
The addresses of the neurons influenced by a neuron are called post-synaptic addresses; the associated weights are called synaptic weights.
For example, an analog elementary neuron can be constituted in the form of a leaky temporal integrator; its internal potential represents the algebraic sum of several potentials applied over time to its inputs by other neurons, this sum being affected by leakage currents; when the internal potential attains a certain threshold, the neuron signals this event by emitting an event signal which is a pulse of very short duration, often called a “spike”. The potential then returns to a rest state, waiting for new inputs. The event signal, or spike, is used, with the address of the neuron which emitted it, to extract the content of the elementary memory associated with this address; this content consists of one or more post-synaptic addresses and their associated synaptic weights. These addresses and weights are received by a processing circuit which formulates weighted input signals and which transmits them in the guise of input signals to the neurons corresponding to the post-synaptic addresses.
In the prior art as illustrated in FIG. 1, the event signal arising from the discharge of a neuron of the matrix of neurons RN is applied to an address encoder ENC which determines the address of the neuron which generated the event and which dispatches this address on a so-called presynaptic bus Bpre-syn. The pre-synaptic bus is an address bus for the memory. This bus is managed by a controller CTRL which applies this address to the memory MEM and which gathers from the memory one or more post-synaptic addresses and the weights associated with each of them. The controller successively emits the various post-synaptic addresses on a post-synaptic addresses bus Bpost-syn which applies these addresses to an address decoder DEC associated with the matrix of neurons RN. At the same time, the controller dispatches the synaptic weights to a digital analog converter DAC which establishes analog levels as a function of each synaptic weight. An analog signal level assigned a determined synaptic weight is therefore applied to each of the post-synaptic neurons identified by the content of the elementary memory which has been activated by the event signal.
Even if the events are spaced over time, that is to say even if the mean frequency of discharge of the neurons is considerably less than the speed of processing of the events by the controller, there is possible saturation of the controller, the encoders and the decoders, because of the large number of neurons of the matrix. Moreover these circuits are bulky and they consume a great deal of energy.